Skip to content

SNES_Scalar: explicit-index tangent loops, finishing the #457 conversion (#747) #748

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
lmoresi wants to merge 1 commit into
base: development
Choose a base branch
from
Open

SNES_Scalar: explicit-index tangent loops, finishing the #457 conversion (#747) #748

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
44 changes: 38 additions & 6 deletions src/underworld3/cython/petsc_generic_snes_solvers.pyx
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3786,12 +3786,44 @@ class SNES_Scalar(SolverBaseClass):
f0_jac = self._jacobian_source(f0)
F1_jac = self._jacobian_source(F1, self._newton_flux(F1))

G0 = sympy.derive_by_array(f0_jac, U)
G1 = sympy.derive_by_array(f0_jac, L)
G2 = sympy.derive_by_array(F1_jac, U)
G3 = sympy.derive_by_array(F1_jac, L)

# Re-organise if needed / make hashable
# Explicit-index Jacobian construction. `sympy.derive_by_array` is
# dx-FIRST (the derivative indices lead), so `derive_by_array(F1, L)`
# yields [dg][df] = dF1[dg]/dL[df] — the TRANSPOSE of the g3 PETSc
# wants. For a scalar unknown that is invisible whenever the flux
# tensor is symmetric (every shipped model: scalar kappa, and
# AnisotropicDiffusionModel's diagonal kappa), and wrong as soon as it
# is not. Same defect as issue #457 in the Stokes/vector tangents,
# fixed there by PR #493; this is the solver that conversion missed
# (#747). Layout contract:
# docs/developer/subsystems/petsc-jacobian-layout.md
#
# derive_by_array carries a second hazard these loops avoid: handed a
# flux built from a matrix-valued symbol it returns an array whose
# .shape disagrees with its backing store, which surfaces later as a
# bare IndexError from sympy internals naming nothing useful.
#
# Shapes are unchanged from the previous form, so the JIT sees the same
# flat sequences: G0 (1,1), G1 (cdim,1), G2 (1,cdim), G3 (cdim,cdim).
# Only G3's index order moves.
Uc = U[0]
G0 = sympy.zeros(1, 1)
G0[0, 0] = sympy.diff(f0_jac[0], Uc)

# G1[df, 0] = d f0 / d L[df]
G1 = sympy.zeros(cdim, 1)
for df in range(cdim):
G1[df, 0] = sympy.diff(f0_jac[0], L[df])

# G2[0, df] = d F1[df] / d U
G2 = sympy.zeros(1, cdim)
for df in range(cdim):
G2[0, df] = sympy.diff(F1_jac[df], Uc)

# G3[df, dg] = d F1[df] / d L[dg]
G3 = sympy.zeros(cdim, cdim)
for df in range(cdim):
for dg in range(cdim):
G3[df, dg] = sympy.diff(F1_jac[df], L[dg])

self._G0 = sympy.ImmutableMatrix(G0)
self._G1 = sympy.ImmutableMatrix(G1)
Expand Down
Loading